research-article

ASRTest: automated testing for deep-neural-network-driven speech recognition systems

Authors:

Zhenyu ChenAuthors Info & Claims

ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 189 - 201

https://doi.org/10.1145/3533767.3534391

Published: 18 July 2022 Publication History

Abstract

With the rapid development of deep neural networks and end-to-end learning techniques, automatic speech recognition (ASR) systems have been deployed into our daily and assist in various tasks. However, despite their tremendous progress, ASR systems could also suffer from software defects and exhibit incorrect behaviors. While the nature of DNN makes conventional software testing techniques inapplicable for ASR systems, lacking diverse tests and oracle information further hinders their testing. In this paper, we propose and implement a testing approach, namely ASR, specifically for the DNN-driven ASR systems. ASRTest is built upon the theory of metamorphic testing. We first design the metamorphic relation for ASR systems and then implement three families of transformation operators that can simulate practical application scenarios to generate speeches. Furthermore, we adopt Gini impurity to guide the generation process and improve the testing efficiency. To validate the effectiveness of ASRTest, we apply ASRTest to four ASR models with four widely-used datasets. The results show that ASRTest can detect erroneous behaviors under different realistic application conditions efficiently and improve 19.1% recognition performance on average via retraining with the generated data. Also, we conduct a case study on an industrial ASR system to investigate the performance of ASRTest under the real usage scenario. The study shows that ASRTest can detect errors and improve the performance of DNN-driven ASR systems effectively.

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Index Terms

ASRTest: automated testing for deep-neural-network-driven speech recognition systems
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2022

808 pages

ISBN:9781450393799

DOI:10.1145/3533767

General Chair:
Sukyoung Ryu
KAIST, South Korea
,
Program Chair:
Yannis Smaragdakis
University of Athens, Greece

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ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 18 - 22, 2022

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Jiang WZhai JMa SZhang XShen C(2024)COSTELLO: Contrastive Testing for Embedding-Based Large Language Model as a Service EmbeddingsProceedings of the ACM on Software Engineering10.1145/36437671:FSE(906-928)Online publication date: 12-Jul-2024
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Yang ZHuang SZheng CWang XWang YXia C(2023)MetaLiDAR: Automated metamorphic testing of LiDAR‐based autonomous driving systemsJournal of Software: Evolution and Process10.1002/smr.2644Online publication date: 20-Dec-2023
https://doi.org/10.1002/smr.2644
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https://dl.acm.org/doi/10.1145/3551349.3556919

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